Abandoned Object Detection via Temporal Consistency Modeling and Back-Tracing Verification for Visual Surveillance
Abstract— An effective approach for detecting < Final Year Projects 2016 > abandoned luggage in surveillance videos. We combine short- and long-term background models to extract foreground objects, where each pixel in an input image is classiﬁed as a 2-bit code. Subsequently, we introduce a framework to identify Static foreground regions based on the temporal transition of code patterns, and to determine whether the candidate regions contain abandoned objects by analyzing the back-traced trajectories of luggage owners. The experimental results obtained based on video images from 2006 Performance Evaluation of Tracking and Surveillance and 2007 Advanced Video and Signal-based Surveillance databases show that the proposed approach is effective for detecting abandoned luggage, and that it outperforms previous methods.
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